Coupled image lattice-based recoverable heterogeneous network cascade failure method and device, and storage medium

A heterogeneous network and cascading failure technology, which is applied in the field of transportation network, can solve the problems such as the degree of interpretation of the reality of the model needs to be strengthened, the correlation is not considered enough, and it is difficult to accurately reflect the real situation. The effect of strong explanatory power

Pending Publication Date: 2021-09-07
CHANGAN UNIV
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  • Abstract
  • Description
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  • Application Information

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Problems solved by technology

After counting CML-related research, the failure state judgment in the process of network failure is often simplified at the expense of the convenience of simulation, and in the overall failure mechanism, an important class of network behavior—recovery behavior has not been extensively discussed. For models that include Insufficient consideration of the correlation between the various elements of , therefore, the degree of realistic interpretation of the existing model still needs to be strengthened, and it is difficult to accurately reflect the real situation

Method used

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  • Coupled image lattice-based recoverable heterogeneous network cascade failure method and device, and storage medium
  • Coupled image lattice-based recoverable heterogeneous network cascade failure method and device, and storage medium
  • Coupled image lattice-based recoverable heterogeneous network cascade failure method and device, and storage medium

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Embodiment 1

[0070] A cascading failure method for recoverable heterogeneous networks based on coupled mapping lattices, specifically in accordance with the following steps:

[0071] S1: Select the rail network data of Xi’an: consider the subway station as a node of the subway network, connect the front and rear stations of the same line, and abstract the urban rail transit network into a weighted and undirected network in view of the two-way mobility of traffic. There are currently 8 subway lines and 150 stations in the selected urban public transportation. The network topology is as follows: image 3 shown.

[0072] S2: There are two types of coupling strength at any node, ε 1 Indicates the structural coupling strength, ε 2 Indicates the flow coupling strength, and the corresponding values ​​of the coupling strength of all nodes are consistent, that is, the structural coupling strength of all nodes ε 1 The same, the flow coupling strength ε of all nodes 2 Similarly, as an important v...

Embodiment 2

[0078] Based on the rail transit network framework selected in Example 1, select 4 types of nodes with a node degree of 1-4 in the network, and substitute them into the HCML model to carry out deliberate single-point attacks, ε 1 =ε 2 = 0.4, considering the topological elements and traffic elements as the top two elements of the network, this embodiment assigns the same weight to the two types of elements that meet the constraint conditions; within 100 time steps, the relationship between the network failure scale G and the iteration time t is calculated , the simulation results are as Figure 5 shown. The results show that nodes with a large attack node degree have a significant impact on the overall failure degree of the network than nodes with a small attack node degree. It shows that the Dadu node is attacked, that is, the node with the strong recovery ability set in the model fails first (at the beginning of the attack, the first step gives the Dadu node an attack beyon...

Embodiment 3

[0080] Based on the environment set in Example 2, select the same attack, and apply the non-resilience coupling map lattice (N-CML) model, low resilience (L-HCML) model and high resilience (H-HCML) model to measure and calculate respectively Network failure scale, the experimental results are as follows Figure 6 shown. N-CML means a CML model that does not consider recovery, that is, formula (5) that does not include the H item; L-HCML means a low resilience (Low) HCML model, that is, h in formula (5) 0 The value range is (1,5), here 4 is taken; H-HCML represents the HCML model with high resilience (High), that is, h in formula (5) 0 The value range is [5,10), and 8 is used here. The results show that the time for the L-HCML and H-HCML models to reach the global collapse situation is much shorter than that of the CML model. When the basic recovery ability of the model is improved h 0 When the value is , the invulnerability of the network increases significantly and can be ...

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Abstract

The invention discloses a coupled image lattice-based recoverable heterogeneous network cascade failure method and device, and a storage medium. The method specifically comprises the steps that a site of a traffic network is taken as a network node, front and rear sites of the same line are taken as a connection edge, and a weighted undirected network is constructed; the time step t is larger than 1, all nodes in the network are in a normal state within the time step 0-t, and when the time step t is t, based on a recoverable coupling map grid model HCML, a network failure result under the action of external disturbance R is determined according to the HCML, and the network cascade failure scale is measured. According to the invention, the cascade failure dynamic process caused by node recovery characteristics is fully considered, and the cascade behavior of a real network is described more comprehensively and more accurately.

Description

technical field [0001] The invention belongs to the technical field of traffic networks, and relates to a recoverable heterogeneous network cascading failure method, equipment and storage medium based on a coupling map grid. Background technique [0002] Cascading failures, also known as cascading failures, are a form of fault propagation in complex networks. Specifically: it is a dynamic behavior in which the nodes or edges in the network in a normal state fail suddenly and cause the abnormal state of the nodes or edges in the adjacent area and continue to spread to a certain range. The generation and expansion of this behavior are mainly affected by two factors: the attribute of the network object and the diffusion mechanism. The attribute of the object includes various factors such as the capacity, load, strength, and tolerance of the node (edge). Various constraints make the network cascading behavior exhibit rich and complex characteristics, and give researchers more f...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/18G06F30/27
CPCG06F30/18G06F30/27
Inventor 王秋玲徐志刚朱璋元闵海根
Owner CHANGAN UNIV
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